In the following experiments, we train a Random Forest [1]} using the scikit-learn python module [2]}. We set the maximum tree depth at 5, the number of trees to 500, and measure the quality of the split using entropy. The class weight is balanced to be robust against size discrepancies between \(\mathcal {C}\)  and \(\mathcal {G}\) .
